The normal process operation is fundamental for safety reasons and in order to obtain quality products with maximum profits. Degradation and/or fault in processes are a threat to said aspects and therefore the prevention thereof is an object of the present invention.
Examples of patent documents related to devices for diagnosing faults in equipment and sensors in continuous and discontinuous processes are U.S. Pat. Nos. 6,298,454, 6,356,857, 6,615,090, 7,421,351, 7,451,003, AR 063876 B1 and AR 071423 A1.
In general, these devices comprise several modules or means, said terms being used interchangeably in the present specification, such as: a data storage module, e.g. of the magnetic type; a data filter module, e.g. of the temporal average type; a modeling module that generates a behavior model of the process in normal conditions; a residual calculation module that calculates the differences between the measured value and the predicted value of variables; a decision module for determining the necessity of communication and the kind of results to be delivered (detection, identification or diagnosis) and a displaying module for displaying a process status report.
The modules disclosed in the prior art, generally interact so as to acquire a set of process historical data, corresponding to a period long enough to register most part of the process operating normal conditions. In time, data are filtered in the filtering module prior to generating the model in the modeling module. The monitoring process generally comprises the following steps:                a) measuring the values of the process variables corresponding to the time period to be analyzed;        b) filtering the values in the filtering module;        c) comparing the filtered values to those of models in the modeling module;        d) using the residuals between the predicted and measured values for calculating fault likelihoods of different abnormal situations, the likelihoods being sent to the decision module where they are analyzed for determining any present abnormality and sending the corresponding message.        
The main differences between the various devices disclosed in the prior art for diagnosing faults in a process, equipment or sensors lay in the modeling and decision modules. The functioning and interaction between different modules do not significantly vary between all known apparatuses since many of these modules are commercially available in the market.
Devices with said characteristics are disclosed, e.g. in patents U.S. Pat. Nos. 6,415,276, 6,557,118, 6,615,090, 7,096,153 and 7,451,003.
In the last cited patent, diagnostics are obtained from the existing differences between the measurements obtained by sensors and the expected values according to a mathematical model for said measurements. These mathematical models may be built up from historical data about the process functioning in normal operation conditions. It is a single linear model producing a residual vector, i.e. the differences between the model predicted value and the experimental value.
The most used methods for building up these models are, among others, principal component analysis, principal least squares and linear regression. A detailed description of said methods can be found in Multi and Megavariate Data Analysis by Eriksson, Johansson Kettaneh-Wold, Trygg, Wikstrom and Wold (2006) UMETRICS ISBN-10:91-973730-2-8.
Devices with said characteristics are also disclosed, e.g. by Petti T. F., Klein J., Dhurjati P. S., Diagnostic Model Processor: Using Deep Knowledge for Process Fault Diagnosis, AIChE Journal, Vol. 36, No. 4, pp. 565-575, (1990) and in patent application US 2005/02109337 A1.
The present invention allows detecting and diagnosing various types of faults, whether they are single or multiple. With this objective, multiple models are used based on the equations describing the system, such as mass and energy balances, design equations, relations between variables that ensure product quality, among others.
Said models use the correlation between the process variables. Unlike other diagnostic systems based on multiple models (e.g. Petti T. F., Klein J., Dhurjati P. S., Diagnostic Model Processor: Using Deep Knowledge for Process Fault Diagnosis), according to the present invention it is only necessary to indicate which variables participate in the same, but not their explicit equation. This simplifies implementation and allows including relationships even in cases where there is no explicit expression.
From models the residuals of which are statistically significant, according to the present invention, a fault rate of abnormal situations associated with each model is calculated. These rates are then used to build, via a cause-consequence network, the fault diagnosis.
The proposed methodology has several advantages over the use of other models based on physics principles as they do not require the actual calculation of the parameters involved in said models.
Furthermore, the time spent by the person responsible for building the models to indicate the values of constants and parameters can be significant. In the case of the proposed invention the device can work autonomously without specifying anything else than the variables involved in the model.
Regarding Patent AR 063876 B1, one of the closest documents mentioned above, it should be noted that it describes a diagnostic system referring only to the diagnosis of instruments while the system corresponding to the present invention includes instruments and equipment.
Patent AR 063876 B1 relates only to instrument and therefore, causal relationships, a fundamental part of the method of the present invention, are not included. Moreover, the method of patent AR 063876 B1 can be used as a way to calculate the probability of instruments faults as a previous step to the analysis of causal relationships between faults, which is the object of the present invention.
Moreover, regarding application AR 071423 A1, another one of the closest documents mentioned above, the method described therein does not perform a diagnosis but identifies a status of the plant. Thus, the method of application AR 071423 A1 complements the use of the method of the present invention as a way of recognizing a complex fault and deactivate the result communication module to avoid redundant messages to the process operators. Thus, the application AR 071423 A1 relates to a method of acknowledging alarms while the present invention is in charge of diagnosis.